AI Enhancements For Interactive User Experiences

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

What if we could analyze transcripts in minutes, trigger surveys the moment users hit friction, and automatically surface the most critical UX issues linked to business goals? What if research reports built themselves, and previous studies were instantly searchable, summarized, and ready to inform new work? These capabilities are no longer just ideas. With agentic AI, they are becoming part of everyday UX research. What is Agentic AI? Agentic AI refers to systems that go beyond simply responding to prompts. Built on advances in large language models and reasoning engines, these systems can set goals, take action, use tools, adapt based on outcomes, and improve through feedback. In UX research, this means working with intelligent collaborators that can support and improve every part of the research process. Agentic AI in Action One of the most practical applications is in qualitative analysis. An agent can process raw transcripts or open-ended responses, clean the data, identify themes, tag sentiment and emotion, extract meaningful quotes, and create summaries for different user segments. It can also learn from your feedback and refine its outputs over time. This helps researchers move from raw data to insights faster, while allowing more focus on interpretation and strategy. Agents can also handle study logistics. They can draft research materials, manage recruitment and scheduling, and monitor participation. If a question causes confusion during a pilot, the agent can suggest adjustments while the study is still running. Agents can also synthesize data across tools like analytics, surveys, recordings, and tickets. They help find patterns, flag inconsistencies, and generate team-specific summaries that connect behavior and feedback. Prioritizing and Preserving Research Agentic AI can also help prioritize UX issues by estimating their frequency, severity, and business impact. It connects usability problems to outcomes like churn, drop-off, or support volume, helping teams focus where it matters most. In research repositories, agents can tag and organize studies, link findings to features or personas, and bring relevant insights forward when new work begins. This turns research archives into useful, living systems. Smarter Reporting and Sampling Agents can generate tailored reports with the right visuals, quotes, and summaries for each audience. They adjust tone and depth based on role and flag anything unusual worth revisiting. On top of that, they can monitor real-time user behavior and trigger contextual surveys or usability invites when users appear confused or frustrated. This ensures more relevant and timely feedback and allows recruitment to adjust based on who is actually experiencing issues. And don't panic! This isn't about replacing researchers. It's about giving us better tools so we can think bigger, move faster, and focus on what really matters.

I recently saw an AI demo that didn't just feel impressive but felt inevitable. It's a crystal clear preview of how AI agents will revolutionize customer experiences forever. The shift from passive "Q&A" chatbots to proactive, multimodal AI agents will transform digital commerce journeys, especially in high-involvement sectors like electronics, automotive, and home improvement. As Joseph Michael says it right, "This is next-level customer service that understands text, speech, images, and even live video." Traditional customer service chatbots have plateaued. They handle basic queries well enough—but they're nowhere near ready for what customers increasingly demand: proactive, personalized, multimodal interactions. As Patrick Marlow (doing the demo in this video) puts it beautifully, here in this video, you will see: ✅ A customer points their camera at their backyard plants. The AI instantly identifies each plant, recommending precise care products tailored specifically for those plants. ✅ The customer casually requests landscaping services. The AI schedules an appointment instantly. ✅ When price negotiations occur, a human seamlessly steps in—no awkward handoffs or "please wait while I transfer you." Here's why this matters to your business: 📌 Customer expectations have evolved beyond simple query resolution. They now expect tailored, interactive journeys. 📌 Static chatbots and scripted interactions no longer differentiate your brand; they commoditize it. 📌 Proactive multimodal AI experiences drive deeper engagement, accelerate purchase decisions, and dramatically boost brand preference. At Swirl®, we're already building specialized multimodal AI agents designed precisely for this next generation of customer experiences with a key focus on discovery, search, and purchase. If you're still relying on traditional chatbots, you're already behind. The future isn't chatbots answering questions; it's AI agents proactively curating personalized customer journeys. Is your business ready for this shift? Let's talk... #ArtificialIntelligence #CX #Ecommerce #AIagents